Results 91 to 100 of about 124,946 (301)

Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control

Results in Control and Optimization
This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems.
Vassilios Yfantis, Achim Wagner, Martin Ruskowski   +2 more
doaj   +1 more source

A Geometrically Regularized Gradient‐Damage Model With Orthogonality‐Based Energy Split for Dynamic Anisotropic Compression‐Shear Fracture

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT This study proposes a novel geometrically regularized gradient‐damage model for simulating dynamic mixed‐mode fracture in orthotropic materials with tension–compression asymmetry. In this model, a thermodynamic framework is formulated by incorporating damage dissipation into internal energy evolution, from which the constitutive relation and ...
Hui Li, Shanyong Wang
wiley   +1 more source

Sonified Signals From a Compact FT‐ICR Instrument: A Feasibility Study. I—Data Mapping to an Equal‐Tempered Chromatic Scale

Rapid Communications in Mass Spectrometry, EarlyView.
ABSTRACT Rationale Ions trapped within a Penning cell (ICR) travel periodic orbits whose frequencies are dependent on their mass‐to‐charge ratio and the value of the magnetic field passing through the trap. Fourier transformation (FT‐ICR) decomposes the signal induced in the detection circuit by the rotation of the ions in the cell after the ...
Patrick Arpino, Michel Heninger
wiley   +1 more source

Machine Learning in Quasi-Newton Methods

Axioms
In this article, we consider the correction of metric matrices in quasi-Newton methods (QNM) from the perspective of machine learning theory. Based on training information for estimating the matrix of the second derivatives of a function, we formulate a quality functional and minimize it by using gradient machine learning algorithms.
Vladimir N. Krutikov, Elena M. Tovbis, Predrag S. Stanimirovic, Lev Kazakovtsev, Darjan Karabasevic   +4 more
openaire   +2 more sources

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Key Technical Fields and Future Outlooks of Space Manipulators: A Survey

SmartBot, EarlyView.
This paper systematically reviews the technological development of space manipulators, emphasizing the unique challenges posed by space environments. It examines four areas: structural design, modeling, planning, and control, while introducing typical ground test platforms.
Gang Chen, Zeyuan Huang, Ying Yan, Xiaokang An, Zhikai Zhao, Jirong Lin, Xiangrong Zhao, Xianyuan Gao, Junyi Zong, Xuewen Jia, Zhipeng Yao, Jianliang Liao, Ning Ji   +12 more
wiley   +1 more source

Minimizing electric vehicle charging costs in the microgrid using the BFGS Quasi-Newton Method [PDF]

E3S Web of Conferences
Electric vehicles (EVs) offer a compelling solution for mitigating pollution, addressing environmental alterations, and enhancing energy security.
Badugu Jayababu, Sandhya G., Nageswarareddy G., Kumar K. Vimala   +3 more
doaj   +1 more source

Solving Systems of Non-Linear Equations by Broyden's Method with Projected Updates [PDF]

We introduce a modification of Broyden's method for finding a zero of n nonlinear equations in n unknowns when analytic derivatives are not available.
David M. Gay, Robert B. Schnabel
core  

Machine Learning‐Driven Capillary Microfluidic Design Automation for Programmable Gradient Generation and Antimicrobial Testing

Small, EarlyView.
TCG‐CMDA, a machine learning‐guided capillary microfluidic design automation platform, enables automated design of tree‐shaped concentration gradient generators for programmable mixing of two agents. The pump‐free chip supports synchronized passive flow and programmable gradient formation, providing a practical framework for decentralized point‐of‐care
Mahmood Khalghollah, Azam Zare, Sorosh Abdollahi, Behrouz H. Far, Amir Sanati Nezhad   +4 more
wiley   +1 more source

Comparisons between Newton and Quasi-Newton method in solving unconstrained optimization problems / Naznin Faiqa Khirul Fozi & Hanis Sofia Mohd Rodi [PDF]

, 2019
Newton and Quasi-Newton methods are widely used in solving unconstrained optimization problems. The solution to optimization problems are known as local optimum solutions and global minimum solutions. For Newton method, if the initial points are far from
Mohd Rodi, Hanis Sofia, Khirul Fozi, Naznin Faiqa   +1 more
core